Machine-learning (ML) requires human-labeled “truth” data to train and test. Acquiring and labeling this data can often be the most time-consuming and expensive part of developing trained models of convolutional neural networks (CNN). In this work, we show that an automated workflow using automatically labeled synthetic data can be used to drastically reduce the time and effort required to train a machine learning algorithm for detecting buildings in aerial imagery acquired with low-flying unmanned aerial vehicles. The MSU Autonomous Vehicle Simulator (MAVS) was used in this work, and the process for integrating MAVS into an automated workflow is presented in this work, along with results for building detection with real and simulated images.

In this paper, watercraft and ship simulation is summarized, and the way that it can be extended through realistic physics is explored. A hydrodynamic, data-driven, immersive watercraft simulation experience is also introduced, using the Unreal Engine to visualize a Landing Craft Utility (LCU) operation and interaction with near-shore waves in virtual reality (VR). The VR application provides navigation scientists with a better understanding of how coastal waves impact landing operations and channel design. FUNWAVE data generated on the supercomputing resources at the U.S. Army Corps of Engineers (USACE) Engineering Research and Development Center (ERDC) are employed, and using these data, a graphical representation of the domain is created, including the vessel model and a customizable VR bridge to control the vessel within the virtual environment. Several dimension reduction methods are being devised to ensure that the FUNWAVE data can inform the model but keep the application running in real time at an acceptable frame rate for the VR headset. By importing millions of data points output from the FUNWAVE version 3.4 software into Unreal Engine, virtual vessels can be affected by physics-driven data.